This paper proposes a detection method of driver fatigue by use of electrocardial signals. First, lifting wavelet transform (LWT) was used to reduce signal noise and its effect was confirmed by applying it to the denoising of a white-noise-mixed Lorenz signal. Second, phase space reconstruction was conducted for extracting chaotic features of the measured electrocardial signals. The phase diagrams show fractal geometry features even under a strong noise background. Finally, Kolmogorov entropy, which is a factor reflecting the uncertainty in and the chaotic level of a nonlinear dynamic system, was used as an indicator of driver fatigue. The effectiveness of Kolmogorov entropy in the judgment of driver fatigue was confirmed by comparison with a semantic differential (SD) subjective evaluation experiment. It was demonstrated that Kolmogorov entropy has a strong relationship with driver fatigue. It decreases when fatigue occurs. Furthermore, the influences of delay time and sampling points on Kolmogorov entropy were investigated, since the two factors are important to the actual use of the proposed detection method. Delay time may have significant influence on fatigue determination, but sampling points are relatively inconsequential. This result indicates that real-time detection can be realized by selecting a reasonably small number of sampling points.

An unconstrained monitoring method for a driver’s heartbeat is investigated in this paper. Signal measurement was carried out by using pressure sensors array. Due to the inevitable changes of posture during driving, the monitoring place for heartbeat measurement needs to be adjusted accordingly. An experiment was conducted to attach a pressure sensors array to the backrest of a seat. On the basis of the extreme learning machine classification method, driving posture can be recognized by monitoring the distribution of pressure signals. Then, a band-pass filter in heart rate range is adapted to the pressure signals in the frequency domain. Furthermore, a peak point array of the processed pressure frequency spectrum is derived and has the same distribution as the pressure signals. Thus, the heartbeat signals can be extracted from pressure sensors. Then, the correlation coefficient analysis of heartbeat signals and electrocardio-signals is performed. The results show a high level of correlation. Finally, the effects of driving posture on heartbeat signal extraction are discussed to obtain a theoretical foundation for measuring point real-time adjustment.

This paper addresses the chatter problem in thin-walled part milling, reviewing the advancements in mirror milling technology for thin-walled parts. It provides an in-depth discussion of the fundamental causes of chatter during thin-walled part milling. A comparative analysis of the advantages and disadvantages of empirical, finite element, and analytical models for milling force in thin-walled parts is presented. The paper also discusses the experimental methods, finite element modal analysis, and structural coupling methods used to obtain the frequency response functions of workpieces and tools, comparing their respective strengths and weaknesses. Furthermore, time-domain and frequency-domain methods for stability prediction are analyzed and compared, with a discussion on the future prospects of chatter stability prediction in mirror milling.